|
Description
|
In today's digital age, network security is paramount. This paper introduces a robust web application designed to swiftly identify network attacks and real-time anomalies, enabling users to protect their networks effectively. By leveraging cutting-edge technologies and machine learning, our system provides insights into network accuracy, F1-score, and precision, allowing users to gauge their network's safety. Our intrusion detection system classifies attacks into four categories: Denial of Service (DoS), Probe, Remote-to-User (R2L), and User-to-Root (U2R). Positioned strategically within the network, it monitors data traffic from all connected devices and promptly responds to suspicious activity, ensuring network security. Users receive instant alerts to take immediate action. Key technologies include data preprocessing (LabelEncoder and MinMaxScaler), user-friendly web interface (MERN stack), and enhanced security with Google Earth 2.0 for authentication and MongoDB for secure data storage. The Python-based backend operates on the NSL-KDD dataset, facilitating effective intrusion detection evaluation. To enhance accuracy, our system employs four machine learning algorithms: K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Random Forest, and Long Short-Term Memory (LSTM), offering comprehensive protection against network threats. In summary, this paper presents a potent network intrusion detection system for real-time threat identification and security assessment. By combining advanced technologies and machine learning, it ensures accuracy and reliability, serving as a valuable tool for network administrators and security professionals in safeguarding digital networks from evolving threats. (2025-01-01)
|
